MIXTURES OF FACTOR ANALYZERS AND DEEP MIXTURES OF FACTOR ANALYZERS DIMENSIONALITY REDUCTION ALGORITHMS FOR HYPERSPECTRAL IMAGES CLASSIFICATION

被引：0

作者：

Zhao, Bin ^{[1
]}

Ulfarsson, Magnus O. ^{[1
]}

Sveinsson, Johannes R. ^{[1
]}

Chanussot, Jocelyn ^{[1
,2
]}

机构：

[1] Univ Iceland, Fac Elect & Comp Engn, Reykjavik, Iceland

[2] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

关键词：

Dimensionality reduction; hyperspectral image; factor analysis; mixtures of factor analyzers; deep mixture of factor analyzers; classification;

D O I：

10.1109/igarss.2019.8898002

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This paper presents two dimensionality reduction methods, mixtures of factor analyzers (MFA) and deep mixtures of factor analyzers (DMFA), for classification of hyperspectral image (HSI). DMFA consists of two layers of MFA and can extract more information from HSI than MFA can. The performance of MFA and DMFA dimensionality reduction methods for classification using real HSI is evaluated in this paper. Experimental results are compared to conventional methods like probabilistic principal component analysis and factor analysis and it is shown that MFA and DMFA give better results.

引用

页码：891 / 894

页数：4

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